Detection and calculation of heart rate recovery in non-clinical settings

ABSTRACT

A wearable device measures heart rate recovery of a user in a non-clinical setting. The wearable device comprises a heart rate detector configured to detect heart rate data of the user, an activity sensor configured to detect motion of the user, and a processor. The processor is configured to identify a start of an activity by the user using the motion detected by the activity sensor. Responsive to detecting the start of the activity, the processor monitors the motion detected by the activity sensor to identify an end of the activity. A regression analysis is performed on heart rate data detected by the heart rate detector during a period of time after the end of the activity, and the heart rate recovery of the user is determined using the regression analysis.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 61/923,182, filed Jan. 2, 2014, which is incorporatedherein by reference in its entirety.

BACKGROUND

Heart rate recovery, the rate at which heart rate decreases immediatelyafter exercise, is a measure of cardiovascular health and fitness. Heartrate recovery is often measured in a clinical setting by a specialist.During the clinical test, a patient walks on a treadmill at a pacecontrolled by the specialist to elevate the patient's heart rate, andthe specialist monitors the patient's heart rate throughout the test.Because the test is completed in a controlled clinical environment, thetest is inconvenient and expensive. As such, few individuals undergoheart rate recovery testing, and the test is conducted infrequently forthose individuals who do undergo heart rate recovery testing. However,literature suggests that heart rate recovery improves with exercise andincreased fitness. As a result, it is beneficial for a user to monitorheart rate recovery more frequently than is possible with a clinicaltest.

SUMMARY

A wearable device measures heart rate recovery of a user in anon-clinical setting. The wearable device comprises a heart ratedetector configured to detect heart rate data of the user, an activitysensor configured to detect motion of the user, and a processor. Theprocessor is configured to identify a start of an activity by the userusing the motion detected by the activity sensor. Responsive todetecting the start of the activity, the processor monitors the motiondetected by the activity sensor to identify an end of the activity. Aregression analysis is performed on heart rate data detected by theheart rate detector during a period of time after the end of theactivity, and the heart rate recovery of the user is determined usingthe regression analysis.

The wearable device for measuring heart rate recovery of a user in anon-clinical setting comprises a heart rate detector configured todetect heart rate data of the user, an activity sensor configured todetect motion of the user, and a processor configured to: identify astart of an activity by the user using the motion detected by theactivity sensor; responsive to detecting the start of the activity,monitor the motion detected by the activity sensor to identify an end ofthe activity; perform a regression analysis on heart rate data detectedby the heart rate detector during a period of time after the end of theactivity; and determine the heart rate recovery of the user using theregression analysis.

The method for measuring heart rate recovery of a user in a non-clinicalsetting comprises identifying a start of an activity by a user using amotion detected by an activity sensor, responsive to detecting the startof the activity, monitoring the motion detected by the activity sensorto identify an end of the activity, performing a regression analysis onheart rate data detected by a heart rate detector during a period oftime after the end of the activity, and determining the heart raterecovery of the user using the regression analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments have other advantages and features which willbe more readily apparent from the detailed description, the appendedclaims, and the accompanying figures (or drawings). A brief introductionof the figures is below.

FIG. 1 illustrates a wearable device, according to one embodiment.

FIG. 2 illustrates an alternative view of a wearable device, accordingto one embodiment.

FIG. 3 illustrates another view of a wearable device, according to oneembodiment.

FIG. 4 is a flowchart illustrating a process for measuring heart raterecovery of a user, according to one embodiment.

FIG. 5 illustrates an example data set collected for measuring heartrate recovery, according to one embodiment.

DETAILED DESCRIPTION

The Figures (FIGS.) and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

A wearable device measures heart rate recovery of a user in a convenientmanner suitable for non-clinical settings, such as home-use orambulatory settings. The wearable device is configured to be worn by theuser while exercising or throughout the user's daily activities,continuously monitoring heart rate and activity levels of the user. Thedevice measures the user's heart rate recovery automatically, withoutexplicit input from the user. Because the device is configured to beworn throughout daily activities, the device measures a user's heartrate recovery on a regular basis to monitor the user's physicalcondition and provide regular feedback as to the user's cardiovascularhealth.

FIG. 1 illustrates an example of a wearable device 100. In oneembodiment, the wearable device 100 is a physiological monitoring devicefor monitoring activities of its user and calculating variousphysiological and kinematic parameters, such as activity levels, caloricexpenditure, step counts, heart-rate, and sleep patterns. The wearabledevice 100 is configured to be in close proximity to or in contact witha user. For example, the device 100 may be worn on a user's appendage orportion thereof, e.g., an arm or a wrist. As another example, the device100 may be worn on a user's chest. A fastening system 101 configured tofasten the device 100 to a user's appendage is shown, although thedevice may alternatively be portable rather than worn. For example, oneor more components of the device 100 may be carried in a pocket of aworn garment or affixed to a bag strap or belt. The fastening elements101 may be removable, exchangeable, or customizable. Furthermore,although embodiments are described herein with respect to a wrist-worndevice, other form factors or designed wear locations of the wearabledevice 100 may alternatively be used. For example, embodiments of themethod described herein may be implemented in arm-worn devices,head-worn devices, chest-worn devices, clip-on devices, and so forth.Moreover, the various components of the device 100 described herein mayalternatively be components of two or more devices, rather than enclosedwithin a single device. That is, one or more of the data collection,processing, and display functions described herein may be performed adevice remote from the user. In this case, the separate components ofthe device 100 are communicatively coupled by wired or wirelesscommunication, continuously communicating data between the components ortransferring data at specified times. For example, a wearable componentof the device 100 may continuously communicate data to an externaldevice (e.g. a smartphone), which processes the data. As anotherexample, a user may periodically connect a wearable component of thedevice 100 to an external computing device, such as a user's computer ora remote server, to transfer data collected by the wearable component tothe external computer.

The wearable device 100 includes a display (or screen) 102 and severaluser interaction points 103. The display 102 and user interaction points103 may be separate components of the device 100, or may be a singlecomponent. For example, the display 102 may be a touch-sensitive displayconfigured to receive user touch inputs and display information to theuser. The wearable device may also have a display element such as 102without interaction points, or interaction points 103 without a displayelement such as 102.

It should be noted that the device 100 may include additional componentsnot shown in FIG. 1. In particular, the device 100 includes one or moresensors for monitoring various physiological or kinematic parameters ofthe user of the device 100.

FIG. 2 is a side view of an embodiment of the device 100, showing afastening system 101, a display (or screen) 102, and one or moreprocessors 203. Although not shown, the device 100 may include a displaydriver. In addition, the device 100 also may include a memory (e.g., arandom access memory (RAM) and/or read only memory (ROM)) and/or memorycache as well as a non-transitory storage medium (e.g., a flash memory).The processor 203, drivers, memories, storage medium, and sensors(further described below) may be communicatively coupled through a databus.

Another view of an embodiment of the wearable device 100 is shown inFIG. 3. FIG. 3 shows a view from beneath the device 100, illustratingthe fastening mechanism 101, the processor 203, a heart rate sensor 301,a motion sensor 303, and one or more user interaction points 103 visiblefrom beneath. In other embodiments, the device 100 may include differentor additional sensors, such as an electrodermal activity (EDA) sensor orother skin conductance or sweat sensor, a temperature sensor, a humiditysensor, and/or a hydration sensor.

The heart rate sensor 301 detects heart rate of a user of the device100. In one embodiment, the heart rate sensor 301 is an optical sensormeasuring a rate of blood flow. However, other types of heart ratesensors may alternatively be used, such as a tonometric pulse ratesensor, an electrocardiogram sensor, or the like. In variousembodiments, the heart rate sensor 301 is communicatively coupled to thedevice 100, rather than being a component of the device 100. Forexample, the heart rate sensor 301 may be a component of a chest strapconfigured to be worn on the chest of a user.

The motion sensor 303 detects motion of the user of the device 100 bymeasuring rotational acceleration, motion, position, and/or changes inrectilinear or rotational speed of the device 100. In one embodiment,the motion sensor 303 is an accelerometer measuring acceleration of thedevice 100 in one or more axes. In other embodiments, the motion sensor303 includes a gyroscope monitoring the orientation of the device 100and/or the orientation or activity of the user. A magnetometer may befurther included to calibrate the gyroscope or to providedirection-based functionality. Sonic embodiments of the motion sensor303 include both an accelerometer and a gyroscope, or an accelerometer,gyroscope, and magnetometer.

The processor 203 receives data from the heart rate sensor 301 and themotion sensor 303. Using the data received from the heart rate sensor301, the processor 203 determines heart rate of the user. In oneembodiment, the processor 203 analyzes the data from the heart ratesensor 301 to determine the user's heart rate at periodic intervals,such as every ten seconds. Using the data received from the motionsensor 303, the processor 203 derives various parameters relating to themotion of the user, such as patterns in the user's movements andmagnitude, frequency, and duration of the movements. The motion sensordata is used to determine an activity level of the user, whichquantifies intensity of an activity based on the detected magnitude andduration. In one embodiment, the processor 203 is configured to identifya type of activity in which the user is engaged based on the datareceived from the motion sensor 303. For example, the processor 203identifies if a user is walking or running based on patterns in theuser's movement derived from the motion sensor data. A process foridentifying activities in which the user is engaged is described in U.S.Provisional Patent Application No. 61/899,848, filed Nov. 4, 2013, whichis incorporated herein by reference in its entirety. In one embodiment,the processor 203 is configured to modify the device 100 based on thedetected activity. For example, a process for modifying a display of awearable device based on a user's activity is described in U.S.Provisional Patent Application No. 61/727,074, filed Nov. 15, 2012,which is incorporated herein by reference in its entirety.

Using the motion data and heart rate, the processor 203 executes analgorithm to measure heart rate recovery of a user of the device 100.The processor 203 may automatically execute the heart rate recoveryalgorithm in response to detecting a sequence of events emulating aclinical heart rate recovery test, without the user of the device 100explicitly providing input to execute the algorithm. In particular, theprocessor 203 executes the heart rate recovery algorithm in response todetecting a sudden decrease in the user's activity level and a lowmotion recovery period following a period of time in which the user'sactivity level was above a threshold. It is noted that the algorithmsare embodied as instructions that are stored within the storage mediumand/or ROM, are loadable into the memory (e.g., RAM), and are executableby the processor 203.

The process 400 executed by the processor 203 for calculating heart raterecovery is illustrated in FIG. 4. It is noted that the process 400 maybe performed substantially in real-time, or may be performedretroactively on data collected by the device 100. For example, theprocessor 203 may perform the process 400 once per day by analyzing datacollected over the course of the day. The process 400 is discussed withreference to FIG. 5, which illustrates an example set of data collectedby the processor 203 during the process 400. It is noted that theprocesses can be embodied as instructions that are stored within thestorage medium and/or ROM, are loadable into the memory (e.g., RAM), andare executable by the processor 203.

Continuing on, the processor 203 monitors 402 user activity by sensorsof the device 100, and detects 404 when the user starts an activity. Inone embodiment, the processor 203 monitors 402 the activities using themotion sensor 303 to detect increases in the user's activity level abovean activity threshold. An activity threshold may be a thresholdmagnitude or duration of sustained activity, and the time at which theactivity level increases above the activity threshold is identified asthe start time of the activity. In one embodiment, the processor 203uses activity data from the motion sensor 303 to identify a type ofactivity in which the user is engaged, such as walking or running, aswell as a time at which the user started the activity of that type. Ifthe user activity does not exceed the activity threshold, the processor203 continues to monitor 402 the user activity until the threshold ismet. After the processor 203 has detected 404 a start of an activity,the processor 203 monitors the user activity data to detect 406 an endof the activity. In one embodiment, the detected activity end is a timeat which the user's activity level falls below the activity threshold.

By monitoring the user activity using the motion sensor 303, theprocessor 203 determines precisely when the user begins and stops anactivity. However, in other embodiments, the processor 203 monitors 402user activities indirectly using other sensors of the device 100, suchas the heart rate sensor 301 or an EDA sensor. For example, theprocessor 203 detects 404 the start of an activity using the user'sheart rate. The time at which the user's heart rate exceeds a thresholdvalue is identified as a time at which the user started an activity. Theheart rate threshold may be a preset value or a minimum increase abovethe user's resting heart rate. Alternatively, the heart rate thresholdmay be a target heart rate, expressed as a percentage of the user'smaximum heart rate as determined according to the user's weight and age.Similarly, the processor 203 detects 406 an end of the activity when theuser's heart rate falls below a predefined threshold.

An example set of heart rate data 505 and activity data 510 collected bythe processor 203 is shown in FIG. 5. For simplicity, the user'sactivity is illustrated in FIG. 5 as a binary activity level value inwhich the activity level is a value of 1 while the user's activity isabove the activity threshold and is a value of 0 when the user'sactivity is below the activity threshold. Each heart rate data pointshown in FIG. 5 is a sample of the user's heart rate, and is generatedby the processor 203 by periodic analysis of the data from the heartrate sensor 301. At time T_(i), the user begins an activity. In responseto the activity, the user's heart rate increases before leveling off atan elevated value. At time T₀, the user ceases the activity, and theuser's heart rate begins to decrease. The period of time after T₀ is a“recovery period,” in which the user's heart recovers from an elevatedheart rate of the active period. In some embodiments, the recoveryperiod is a fixed length of time, such as 30 or 60 seconds.

The processor determines 408 whether the period of time after the end ofthe activity (that is, after T₀) is an adequate recovery period forassessing the user's heart rate recovery. A recovery period that isadequate for assessing heart rate recovery is one in whichphysiologically relevant heart recovery parameters can be derived fromdata collected during the recovery period. To determine 408 whether theinterval following the end of the activity is a recovery period adequatefor measuring the user's heart rate recovery, the processor 203 mayanalyze several factors, such as the length of time the user's activitylevel was above the activity threshold, the number of heart rate datapoints collected during a specified interval of the recovery period, howquickly the user's activity level decreased, and whether the user'sactivity level remained below the activity threshold for a sufficientlength of time. For example, the processor 203 determines a recoveryperiod to be adequate if the activity level was above the activitythreshold for at least a threshold length of time, but determines therecovery period is not adequate for measuring heart rate recovery if theactive period is shorter than the threshold length of time. As anotherexample, the processor 203 determines that the recovery period is notadequate for measuring heart rate if the user resumes an activity abovethe activity threshold before a. specified length of time has passed. Ifthe recovery period is inadequate for measuring heart rate recovery, theprocessor 203 resumes monitoring 402 the user's activity level to detect404 the beginning of another activity.

If the recovery period is adequate for measuring heart rate recovery,the processor 203 analyzes 410 the heart rate data collected during therecovery period. In one embodiment, the processor 203 analyzes 410 theheart rate data by performing an exponential or sigmoidal regression forthe heart rate data collected during the recovery period. For example,the processor 203 may determine values for coefficients A and λ to fitan equation of the format:

HR=Ae ^(−λt)  (1)

to the heart rate data collected during the recovery period, where HR isthe user's heart rate at time t. In one embodiment, the goodness of fitof the curve fitting is used to determined 408 whether the recoveryperiod is adequate for measuring heart rate recovery. Furthermore, byfitting an equation to the heart rate samples, the processor 203estimates the user's heart rate between samples by interpolation. Forexample, if one or more heart rate samples are missed during therecovery period or if the recovery period is shorter than a desiredthreshold length, the processor 203 estimates the missing values usingthe curve fit. The processor 203 may also determine the maximum heartrate HR_max and minimum heart rate HR_min measured after the cessationof activity by identifying the maximum and minimum values measuredduring the recovery period or a specified portion of the recoveryperiod, or by calculating an expected maximum or minimum using the curvefit. In other embodiments, the processor 203 analyzes 410 the heart ratedata using a correlation between time and heart rate, or using agradient calculated by subtracting adjacent heart rate values. Theanalysis 410 may further include analyzing the intensity of the user'sactivity using data from the heart rate sensor 301 and/or the motionsensor 303, and analyzing environmental parameters measured by atemperature, humidity, or hydration sensor. If the recovery period issufficiently long, the processor 203 may perform the analysis 410 onheart rate data collected within a specified portion of the recoveryperiod, such as the first minute after the end of the activity. Forexample, time T₁ in FIG. 5 represents the end of the portion of therecovery period over which the processor 203 performs the analysis.

Using the analysis 410, the processor 203 determines 412 heart recoverymetrics for the user. In one embodiment, the processor 203 uses theexponential or sigmoidal curve fitting to determine 412 the user's heartrecovery metrics. For example, the value of λ in equation (1) representsa rate of decrease of the user's heart rate during the recovery period,where a large value of λ indicates that the user's heart rate decreasesrapidly after exercise. The value of λ is therefore output as ameasurement of the user's heart rate recovery in some embodiments. Inthis case, the processor 203 may also output the goodness of fit of thedetermined curve fitting, or may use the goodness of fit to assessaccuracy of the calculated heart rate recovery metric. For example, ifthe goodness of fit is below a threshold value, the processor 203 doesnot output the calculated value of λ.

In another embodiment, the processor 203 outputs the difference betweenHR_max and HR_min as a measure of the user's heart rate recovery. Ifheart rate samples are missing during the recovery period, such thatHR_max and/or HR_min are not directly measured, the processor 203calculates the expected heart rate at time T₀ or T₁ using the curve fitto estimate the value of HR_max or HR_min, respectively, and outputs thedifference between the calculated HR_max and HR_min as the user's heartrate recovery. In yet another embodiment, the processor 203 outputs theamount of time for the user's heart rate to decrease by a specifiedamount, such as 30 beats per minute.

Each measurement of the user's heart rate recovery may be individuallystored and analyzed, or the processor 203 may average severalmeasurements of the user's heart rate recovery to improve the accuracyof the metrics. For example, the processor 203 may average measurementscollected over the course of a day and output the average value as theuser's heart rate recovery metric for the day. The processor 203 mayequally weight the measurements when computing the average, or mayweight the measurements using goodness of fit of each measurement orother data quality measures. In the latter case, measurements having ahigher reliability or quality (as determined, for example, using thegoodness of fit) are weighted more heavily than measurements having alower reliability or quality, improving the accuracy of the averagedheart rate recovery metric. Other processing may also be performed onthe heart rate recovery measurements. For example, the processor 203 maynormalize each measurement to intensity level of the correspondingactivity, environmental parameters measured by a temperature, humidity,or hydration sensor,or orientation of the user during the activity andrecovery period.

The process 400 for measuring a user's heart rate recovery may beperformed repeatedly for the same user to monitor the user's physicalfitness over time. In one embodiment, the processor 203 continuouslymonitors the user's activity levels and measures the user's heart raterecovery any time sufficient activity levels and recovery periods aredetected. Alternatively, the processor 203 may perform the heart raterecovery measurement process 400 at periodic intervals, such as once perweek, or when a user provides an input to capture a heart rate recoverymeasurement.

The processor 203 may output the user's heart rate recovery metric tothe display 102 of the wearable device and/or store the metric forfuture analysis. In one embodiment, the user's heart rate recoverymetrics are periodically reported over a network to a coach, trainer,physician, physical therapist, or the like, who uses the heart raterecovery metrics to monitor the user's physical condition over a periodof time. For example, an athlete's heart rate recovery may be reportedto a coaching service, in which a coach uses the athlete's heart raterecovery to assess the athlete's physical condition and design workoutsto improve the athlete's fitness level. As another example, a doctor maymonitor the heart rate recovery of a patient at risk for heart diseaseto prevent or quickly diagnose any potentially dangerous conditions.Similarly, a physical therapist may monitor the heart rate recovery of apatient to quantify the effect of therapy on the patient's physicalcondition. Furthermore, trends in a user's fitness can be predictedusing stored heart rate recovery metrics. For example, a trend canindicate whether a user is becoming more or less fit, and may be used topredict future heart rate recovery values, issue recommendations inadvance of the reduction in fitness, trigger alerts to trainers orcaretakers, or provide an adjusted user experience to correct anyundesirable trends towards lower fitness.

The device and process described herein for measuring heart raterecovery provides convenient, cost-effective monitoring of a user'sphysical condition. Because the process is executed automatically, usersdo not need to visit a specialist or undergo a controlled clinical test.The measurement can therefore be conducted on a more frequent basis thanis feasible for the clinical test. With a larger number of measurementscollected, the heart rate recovery metrics generated by the processdescribed herein provide regular feedback to users and may be lesssusceptible to anomalous measurements.

Additional Configuration Considerations

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. For example, the functionscorresponding to the process steps in FIGS. 4 and 5 may be embodied asdiscrete modules (e.g., one for each function). Modules may constituteeither software modules (e.g., program code (or instructions) embodiedon a machine-readable medium) or hardware modules. A hardware module istangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware modules of a computer system (e.g.,processor 203) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

The various operations of example methods described herein, e.g., suchas those described with FIGS. 3, 4 and 5, may be performed, at leastpartially, by one or more processors, e.g., 203, that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented modules that operate toperform one or more operations or functions. The modules referred toherein may, in some example embodiments, comprise processor-implementedmodules.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some enrbodirnents may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process for measuring heart rate recovery through thedisclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

1. A wearable device for measuring heart rate recovery of a user in anon-clinical setting, the wearable device comprising: a heart ratedetector configured to detect heart rate data of the user; an activitysensor configured to detect motion of the user; and a processorconfigured to: identify a start of an activity by the user using themotion detected by the activity sensor; responsive to detecting thestart of the activity, monitor the motion detected by the activitysensor to identify an end of the activity; perform a regression analysison heart rate data detected by the heart rate detector during a periodof time after the end of the activity; determine the heart rate recoveryof the user using the regression analysis; calculate a goodness of fitof the regression analysis to the heart rate data detected during theperiod of time after the end of the activity; and wherein the heart raterecovery of the user is determined responsive to the goodness of fitbeing above a threshold.
 2. The wearable device of claim 1, wherein theprocessor is further configured to: determine an activity level of theuser using the motion detected by the activity sensor; wherein the startof the activity is identified responsive to the activity level beinggreater than an activity threshold; and wherein the end of the activityis identified responsive to the activity level decreasing below theactivity threshold.
 3. The wearable device of claim 2, wherein theactivity threshold comprises at least one of a magnitude of the motiondetected by the activity sensor and a duration of the motion detected bythe activity sensor. 4-5. (canceled)
 6. The wearable device of claim 1,wherein the processor is further configured to: analyze the period oftime after the end of the activity to determine if the period of time isa recovery period; wherein the regression analysis is performedresponsive to the period of time after the end of the activity being arecovery period.
 7. The wearable device of claim 6, wherein theprocessor is further configured to determine the period of time afterthe end of the activity is a recovery period responsive to a length oftime between the start of the activity and the end of the activity beinggreater than a threshold.
 8. The wearable device of claim 6, wherein theprocessor is further configured to determine the period of time afterthe end of the activity is a recovery period responsive to a length ofthe period of time after the end of the activity being greater than athreshold.
 9. The wearable device of claim 6, wherein the processorfurther configured to determine the period of time after the end of theactivity is a recovery period responsive to a number of data points ofthe heart rate data detected during the period of time being greaterthan a threshold.
 10. (canceled)
 11. A system for measuring heart raterecovery of a user in a non-clinical setting, the system comprising: aheart rate detector configured to detect heart rate data of the user;and a processor configured to: perform a regression analysis on heartrate data detected by the heart rate detector; determine the heart raterecovery of the user using the regression analysis; calculate a goodnessof fit of the regression analysis to the heart rate data detected duringthe period of time after the end of the activity; and wherein the heartrate recovery of the user is determined responsive to the goodness offit being above a threshold.
 12. The system of claim 11, wherein theheart rate data is detected during an activity performed by the user,and wherein the processor is further configured to: analyze a period oftime after an end of the activity to determine if the period of time isa recovery period; wherein the regression analysis is performedresponsive to the period of time after the end of the activity being arecovery period.
 13. The system of claim 12, wherein the processor isfurther configured to determine the period of time after the end of theactivity is a recovery period responsive to a length of time between astart of the activity and the end of the activity being greater than athreshold.
 14. The system of claim 12, wherein the processor is furtherconfigured to determine the period of time after the end of the activityis a recovery period responsive to a length of the period of time afterthe end of the activity being greater than a threshold.
 15. The systemof claim 12, wherein the processor is further configured to determinethe period of time after the end of the activity is a recovery periodresponsive to a number of data points of the heart rate data detectedduring the period of time being greater than a threshold.
 16. (canceled)17. A method for measuring heart rate recovery of a user in anon-clinical setting, the method comprising: identifying a start of anactivity by a user using a motion detected by an activity sensor;responsive to detecting the start of the activity, monitoring the motiondetected by the activity sensor to identify an end of the activity;performing a regression analysis on heart rate data detected by a heartrate detector during a period of time after the end of the activity;determining the heart rate recovery of the user using the regressionanalysis; calculating a goodness of fit of the regression analysis tothe heart rate data detected during the period of time after the end ofthe activity; and wherein the heart rate recovery of the user isdetermined responsive to the goodness of fit being above a threshold.18. The method of claim 17, further comprising: determining an activitylevel of the user using the motion detected by the activity sensor;wherein the start of the activity is identified responsive to theactivity level being greater than an activity threshold; and wherein theend of the activity is identified responsive to the activity leveldecreasing below the activity threshold.
 19. The method of claim 17,wherein the start of the activity is identified responsive to a heartrate of the user increasing above a threshold heart rate, and whereinthe end of the activity is identified responsive to the heart rate ofthe user falling below the threshold heart rate.
 20. The method of claim17, further comprising: analyzing the period of time after the end ofthe activity to determine if the period of time is a recovery period;wherein the regression analysis is performed responsive to the period oftime after the end of the activity being a recovery period.
 21. Themethod of claim 17, wherein the activity threshold comprises at leastone of a magnitude of the motion detected by the activity sensor and aduration of the motion detected by the activity sensor.
 22. The methodof claim 20 further comprising determining the period of time after theend of the activity is a recovery period responsive to a length of timebetween the start of the activity and the end of the activity beinggreater than a threshold.
 23. The method of claim 20 further comprisingdetermining the period of time after the end of the activity is arecovery period responsive to a length of the period of time after theend of the activity being greater than a threshold.
 24. The method ofclaim 20 further comprising determining the period of time after the endof the activity is a recovery period responsive to a number of datapoints of the heart rate data detected during the period of time beinggreater than a threshold.